Special Issue "Advanced Remote Sensing Technologies for Disaster Monitoring"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental and Sustainable Science and Technology".

Deadline for manuscript submissions: 30 April 2020.

Special Issue Editors

Dr. Seonyoung Park
E-Mail Website
Guest Editor
Satellite Operation and Application Center, Korea Aerospace Research Institute, Daejeon 34133, Korea
Interests: Drought; Evapotranspiration; Disaster; Environmental Impact Assessment; Soil and Water Conservation; Remote Sensing and GIS, Agriculture
Dr. Jong-min Yeom
E-Mail Website
Guest Editor
Satellite Operation and Application Center, Korea Aerospace Research Institute, Daejeon 34133, Korea
Interests: Disaster monitoring, Fire, land surface application, cloud, vegetation, agriculture, solar energy resource and surface albedo by using sun- and geo- synchronous satellites

Special Issue Information

Dear Colleagues,

For the last decade or so, there has been intense research activity regarding the exploitation of remote sensing technologies in disasters such as drought, extreme temperatures, earthquakes, cyclones, flooding, landslides, wildfires, etc. Climate change is affecting the occurrences of disasters, resulting in the higher vulnerability of regions to severe events. It is important to prevent, mitigate, and recover from disasters by monitoring these disasters using enhanced technologies. Remote sensing is one of such technologies that is suitable to effectively collect data on a large scale with varied spatial, spectral, and temporal resolutions. Many satellite’s data has been employed to monitor disasters, identify the damage of disasters, and assess the recovery of disaster.

This Special Issue invites state-of-the-art research on disaster monitoring using satellite remote sensing data. In this Special Issue, we expect to introduce various studies covering remote sensing technologies that can be applied in disaster monitoring.

Dr. Seonyoung Park
Dr. Jong-min Yeom
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Monitoring natural hazards
  • Landslides and land degradation
  • Climate change
  • Land use and land cover change
  • Typhoon
  • Droughts
  • Floods, and floodplains
  • Earthquakes
  • Tsunamis
  • Hazard and vulnerability assessments
  • Risk mapping
  • Early warning systems

Published Papers (1 paper)

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Research

Open AccessArticle
Discrimination of Earthquake-Induced Building Destruction from Space Using a Pretrained CNN Model
Appl. Sci. 2020, 10(2), 602; https://doi.org/10.3390/app10020602 - 14 Jan 2020
Abstract
The building is an indispensable part of human life which provides a place for people to live, study, work, and engage in various cultural and social activities. People are exposed to earthquakes, and damaged buildings caused by earthquakes are one of the main [...] Read more.
The building is an indispensable part of human life which provides a place for people to live, study, work, and engage in various cultural and social activities. People are exposed to earthquakes, and damaged buildings caused by earthquakes are one of the main threats. It is essential to retrieve the detailed information of affected buildings after earthquakes. Very high-resolution satellite imagery plays a key role in retrieving building damage information since it captures imagery quickly and effectively after the disaster. In this paper, the pretrained Visual Geometry Group (VGG)Net model was applied for identifying collapsed buildings induced by the 2010 Haiti earthquake using pre- and post-event remotely sensed space imagery, and the fine-tuned pretrained VGGNet model was compared with the VGGNet model trained from scratch. The effects of dataset augmentation and freezing different intermediate layers were also explored. The experimental results demonstrated that the fine-tuned VGGNet model outperformed the VGGNet model trained from scratch with increasing overall accuracy (OA) from 83.38% to 85.19% and Kappa from 60.69% to 67.14%. By taking advantage of dataset augmentation, OA and Kappa went up to 88.83% and 75.33% respectively, and the collapsed buildings were better recognized with a larger producer accuracy of 86.31%. The present study showed the potential of using the pretrained Convolutional Neural Network (CNN) model to identify collapsed buildings caused by earthquakes using very high-resolution satellite imagery. Full article
(This article belongs to the Special Issue Advanced Remote Sensing Technologies for Disaster Monitoring)
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